Measurement of electrophysiologic response

ABSTRACT

A method for estimating an electrophysiologic response contained in a measured signal includes obtaining a plurality of samples and defining a plurality of bins, each of which corresponds to a range of values of a sorting parameter associated with each of the samples. Each sample of the measured signal is then classified into one of the bins on the basis of a value of a sorting parameter associated with that sample. Then, for each bin, a statistic indicative of samples classified into that bin is maintained. On the basis of these bin statistics, the desired electrophysiologic response can then be estimated.

RELATED APPLICATIONS

This application claims the benefit of the priority date of U.S.Provisional Application No. 60/243,682, filed on Oct. 27, 2000, thecontents of which are herein incorporated by reference.

FIELD OF INVENTION

The invention relates to the measurement of electrophysiologicresponses, and more particularly to enhancing the signal-to-noise ratioin such measurements.

BACKGROUND

In making a diagnosis, it is often useful to have the patient'scooperation. This is particularly true in the diagnosis of diseaseinvolving sensory pathways to the brain. For example, a straightforwardway to assess a patient's hearing is to simply ask the patient whetherhe can hear particular tones having various frequencies and amplitudes.

In many cases, one takes for granted that a patient will be able toanswer such questions. However, in some cases, a patient cannotcommunicate his perception. This occurs most frequently when the patientis an infant, or when the patient is unconscious. In a veterinarysetting, it is rare to encounter a patient that can accuratelycommunicate perception at all.

One approach to evaluating an infant's hearing is to make a sound and tothen measure an evoked response associated with that sound. This evokedresponse is typically an electrophysiologic signal generated in responseto the sound and traveling between the inner ear and the brain alongvarious neural pathways, one of which includes the auditory brainstem.This signal is thus referred to as the “auditory brainstem-response,”hereafter referred to as the “ABR.”

The ABR is typically only a small component of any measuredelectrophysiologic signal. In most cases, a noise component arising fromother, predominantly myogenic, activity within the patient dwarfs theABR. The amplitude of the ABR typically ranges from approximately 1microvolt, for easily audible sounds, to as low as 20 nanovolts, forsounds at the threshold of normal hearing. The noise amplitude presentin a measured electrophysiologic signal, however, is typically muchlarger. Typical noise levels range from between 2 microvolts to as muchas 2 millivolts. The resulting signal-to-noise ratio is thus between −6dB and −100 dB

One approach to increasing the signal-to-noise ratio is to exploitdifferences between the additive properties of the ABR and that of thebackground noise. This typically includes applying a repetitive auditorystimulus (a series of clicks, for example) and sampling theelectrophysiologic signal following each such stimulus. The resultingsamples are then averaged. The ABR component of the samples addlinearly, whereas the background electrophysiologic noise, beingessentially random, does not. As a result, the effect of noise tends todiminish with the number of samples. The number of samples required toreach a specified signal-to-noise level depends on the noise levelpresent in the samples. In principle, therefore, one can achieve aspecified signal-to-noise ratio either with a small number of relativelyquiet samples or with a large number of relatively noisy samples.

In practice, signal averaging techniques such as that described aboveare unlikely to work when the signal-to-noise ratio is worse than −48dB. Since a minimally acceptable 5% confidence level requires asignal-to-noise ratio of at least −4 dB, this signal-averaging approachis prone to inaccuracy.

Signal averaging methods as described above perform best when thebackground noise is relatively constant. For example, the steady droneof an air-conditioner can readily be separated from a signal ofinterest. Such background noise is referred to as “stationary” noise.

The noise component of an electrophysiologic signal is oftennon-stationary. For example, after a few minutes of taking measurements,an infant may begin to stir, thereby momentarily increasing thebackground electrophysiologic noise level. The infant might then returnto a deep sleep, thereby reducing the background electrophysiologicnoise level.

The non-stationary nature of the noise component poses a dilemma for aclinician attempting to measure the ABR. For example, if the infantbegins to stir, the clinician might suspend taking measurements to avoidcontaminating data already collected with noisy data. This might proveto be a good decision if the infant were to fall back into a deep sleep,since one could then acquire additional quiet samples. However, evennoisy samples can improve signal-to-noise ratio, provided that there areenough of them available. Hence, this might also prove to be a poordecision if the infant were to continue stirring. In such a case, itwould have been better to have acquired the additional, albeit noisysamples. Because the behavior of an infant is, to a great extent,unpredictable, the clinician occasionally makes an incorrect guess,thereby either wasting time or needlessly corrupting acquired data.

SUMMARY

The invention is based on the recognition that, by dividing the sequenceof samples that make up the signal into subsequences of samples, one canreduce the signal-to-noise ratio of an electrophysiologic signal andavoid many difficulties posed by the presence of non-stationary noise.The samples within a particular subsequence are characterized by acommon range of values of a sorting parameter. Each subsequence ofsamples yields a statistic that is independent of correspondingstatistics yielded by other subsequences of samples. These statistics,each of which corresponds to a subsequence, can then be combined indifferent ways to derive an estimate of an electrophysiologic responsecontained in the signal. The presence of non-stationary noise can, to agreat extent, be compensated for by appropriately combining thestatistics associated with each subsequence.

In one practice of the invention, a plurality of samples of a measuredelectrophysiologic signal is obtained. The electrophysiologic signaltypically includes an electrophysiologic response to a stimulus. Themethod of the invention seeks to estimate the value of this response.

The method includes defining a plurality of bins, each of whichcorresponds to a range of values of a sorting parameter associated witheach of the samples. Preferably, the range of values for each bin issuch that each value of the sorting parameter is associated with at mostone bin.

Each sample of the measured signal is then classified into one of thebins on the basis of a value of a sorting parameter associated with thatsample. Then, for each bin, a statistic indicative of samples classifiedinto that bin is maintained. On the basis of these bin statistics, thedesired electrophysiologic response can then be estimated. In oneparticular practice of the invention, maintaining the bin statisticincludes maintaining a moving average of samples in the bin.

In one practice of the invention, the sorting parameter includes ameasure of noise present in the samples. The noise might beelectrophysiologic noise, ambient acoustic noise, or any other noiseprocess. The sorting parameter can also be derived from a combination ofnoise processes.

The estimation of electrophysiologic response can include combining thebin statistics to derive a quantity indicative of the electrophysiologicresponse. This might include averaging the bin statistics, or evaluatinga weighted averaging of the bin statistics, with the weights beingmanually or automatically selected. In one practice, the weight assignedto a statistic for samples in a particular bin might be indicative of aquality of the samples in the bin. For example, the weight can beinversely proportional to a noise level associated with the particularbin. Alternatively, the weights can be selected to optimize a measure ofan extent to which the quantity approximates the electrophysiologicresponse. The assignment of weights in a weighted average can alsoinclude excluding bin statistics associated with particular bins frombeing considered in evaluating the quantity indicative of theelectrophysiologic response.

In another practice of the invention, a sequence of samples isdecomposed into a plurality of subsequences, each of which includessamples selected on the basis of a value of a sorting parameterassociated with each of the samples. The samples from each subsequenceare then used to evaluate a plurality of subsequence statistics, each ofwhich is associated with a corresponding subsequence. A subset of thesesubsequence statistics is then selected. The subset can include some orall of the subsequence statistics. On the basis of subsequencestatistics from this set, the electrophysiologic response is thenestimated.

In one practice of the invention, the subsequences are selected byselecting a noise threshold. Subsequence statistics that are associatedwith subsequences having noise levels above this threshold are thenexcluded from the subset.

The extent to which each of the selected subsequence statisticscontributes to an estimate of the electrophysiologic response can becontrolled. For example, one or more subsequence statistics can beweighted by an amount indicative of noise present in the correspondingsubsequence. In this optional practice of the invention, subsequencesstatistics from subsequences that contain exceptionally noisy samplescan be made to contribute less to the estimate than subsequencestatistics from subsequences having samples that are not as noisy.

The method of the invention is applicable to various types ofphysiological stimuli. These stimuli include auditory, visual,olfactory, and gustatory stimuli, or combinations thereof.

These and other features and advantages of the invention will betterunderstood from the following detailed description and the accompanyingfigures, in which:

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a system for acquiring electrophysiologicdata; and

FIGS. 2 and 3 illustrate the data acquisition process.

DETAILED DESCRIPTION

Referring to FIG. 1, a system 10 for acquiring electrophysiologic datafor measurement of auditory brainstem response (“ABR”) includes a sensor12 attached to an infant's scalp. The sensors 12, which are typicallyscalp electrodes, are configured to detect an analog signal 13representing ongoing electrical activity. This analog signal 13 isprovided to first and second band-pass filters 14 a-b that generatefirst and second filtered signals 15 a-b, respectively. In oneembodiment, the first band-pass filter 14 a has a passband between 180Hz and 2000 Hz and the second band-pass filter 14 b has a passbandbetween 30 Hz and 2000 Hz. The resulting first and second filteredsignals 15 a-b are then passed to first and second analog-to-digital(A/D) converters 18 a-b for conversion into a corresponding first andsecond digital signals 19 a-b. These digital signals 19 a-b are thenprovided to a digital signal processor 20.

Referring now to FIG. 2, on the basis of noise measurements derived fromthe first digital signal 19 a, the digital signal processor 20 sorts thesamples that make up the second digital signal 19 b into a plurality ofbins 22 a-j each of which is associated with a band of noise amplitudes.The amplitude bands of the bins 22 a-j are selected to benon-overlapping. For the application described herein, there are tenbins. However, the number of bins 22 a-j, and the amplitude rangesassociated with each bin 22 a-j, depend on the specific application ofthe data-acquisition system 10. Each bin 22 a-j has an associatedaveraging accumulator 24 a-j that maintains a moving average 25 a-j ofthe samples in its corresponding bin 22 a-j. Each bin 22 a-j also has anassociated counter 27 a-j that contains the number of samples N_(i) inits associated bin 22 a-j. Referring back to FIG. 1, the moving averages25 a-j and the counters 27 a-j are maintained in a data buffer 26 thatis available to a processing system 28.

Note that the first and second digital signals 19 a-b need not use thesame time-base. For example, the first A/D converter 18 a might samplethe first filtered signal 15 a at a sampling rate that differs from thatused by the second A/D converter 18 b to sample the second filteredsignal 15 b. In another example, the noise analysis may be made over aportion of the first filtered signal 15 a that corresponds to a timeinterval that precedes and/or follows the portion of the second filteredsignal 15 b that corresponds to a time interval including the data beingsorted into one of the bins. Additionally, noise analysis of a portionof the first filtered signal 15 a can impact the sorting of samples fromseveral portions of the second filtered signal 15 b. The method of theinvention can thus be used with any manner of noise analysis.

During data acquisition, each averaging accumulator 24 a averages onlythose samples within its associated bin 22 a. Since all samples arewithin one of the bins 22 a-j, each sample can affect no more than onemoving average 25 a-j. Since the samples in any one bin 22 a areaveraged independently of samples in other bins 22 b-j, samples from onebin 22 a are prevented from contaminating the moving averages 25 b-jobtained by averaging samples from other bins 22 b-j.

Referring now to FIG. 3, the clinician can, at any time select which ofthe moving averages 25 a-j available for each band are to be combinedinto a single average representative of an ABR measurement 38. As shownin FIG. 3, the clinician controls switches u_(i) 29 a-j that selectivelyexclude selected bands (hereafter referred to as “excluded bands”) fromconsideration in evaluating the ABR measurement 38. These switches 29a-j are typically set to exclude from consideration all bands having anoise power above a selected threshold.

The clinician also controls weighting coefficients 30 a-j associatedwith each of the remaining bands (hereafter referred to as the“included” bands). These weighting coefficients 30 a-j can be controlledmanually, or automatically. In either case, weighting coefficients 30a-j can be controlled individually, or as a group. Additionally,particular combinations of weighting coefficients 30 a-j can bepre-programmed and selectively applied.

The moving averages 25 a-j of each included band, which are available inthe accumulators 24 a-j, are then multiplied by the corresponding numberof samples N_(l) in each band. The results are then scaled by theircorresponding weighting coefficients 30 a-j at corresponding mixers 32a-j. The outputs 35 a-j of the mixers 32 a-j, which are proportional tothe weighted averages 34 a-j corresponding to each band, the accumulatednumber of samples summed across all included bands, and the sum of theweighting coefficients of the included bands, are then provided to anoutput averaging-element 36, the output of which is the desired ABRmeasurement 38. This ABR measurement 38 is obtained by summing theoutputs of the mixers 32 a-j and normalizing the result by both the sumof the weighting coefficients of the included bands and the accumulatednumber of samples summed across the included bands.

In the illustrated embodiment, the processing system 28 carries out thefunction of mixing the moving averages 25 a-j with the weightingcoefficients 30 a-c, averaging the resulting products, and normalizingthe result to obtain the desired ABR measurement 38. However, withoutloss of generality, these functions can also be carried out byspecial-purpose hardware.

In one practice of the invention, the data associated with each includedband is weighted by the reciprocal of the noise amplitude associatedwith that band. As a result, data from noisier included bands willcontribute less to the ABR measurement 38 than data from less noisyincluded bands. This reduces the possibility that contributions fromnoisier included bands will excessively degrade the accuracy of the ABRmeasurement 38.

In addition to processing the amplified signal received from thesensors, the digital signal processor 20 also generates repetitiveauditory stimuli. These auditory stimuli are communicated to the infantthrough an earphone 40 in communication with the digital signalprocessor 20 by way of a digital-to-analog (D/A) converter 42, as shownin FIG. 1. The auditory stimuli can be adaptively controlled by thedigital signal processor 20 in response to the measurements obtained bythe data-acquisition system 10. For example, if no ABR response appearsto be evoked, the digital signal processor 20 may gradually increase theamplitude of the auditory stimuli to identify the infant's hearingthreshold.

The processing system 28 also executes user-interface software fordisplaying, on a display monitor 48, the results of data manipulationperformed by the digital signal processor 20. In the illustratedembodiment, the processing system 28 uses a Windows NT® operating systemto execute user-interface software necessary for convenient display ofdata.

The data-acquisition system 10 permits retrospective control over whichbands to incorporate into the ABR measurement 38 and the extent to whicheach band contributes to the ABR measurement 38. By judiciouslyselecting the weighting coefficients 30 a-j, the signal-to-noise ratioof the ABR measurement can be optimized even in the presence ofnon-stationary electrophysiologic noise. As the ABR measurement 38unfolds during the data acquisition process, the weighting coefficients30 a-j can be adjusted in an effort to maximize the signal-to-noiseratio of the ABR measurement 38. These adjustments can be made either inreal-time, while the test is being conducted, or after the test has beenterminated. The clinician conducting the test can thus experiment withdifferent weighting coefficients 30 a-j without discarding valuable dataand/or unnecessarily replicating data.

Clinical ABR testing often results in multiple tests of the samestimulus condition, with measurements from each test being contaminatedby different patterns of background nose. For example, in the middle ofone test, a doctor's pager may suddenly go off, while in the middle ofanother test, the infant may cough or sneeze.

Previously, it was counterproductive to combine data from a relativelynoiseless test with data from a test having greater average noise. Thedata-acquisition system 10 described herein, however, permits data to becombined band by band across several such tests in a manner thatoptimizes the signal-to-noise ratio of the resulting ABR measurement 38.

In conventional data-acquisition systems, weighted averaging requires apriori selection of weighting coefficients. Thus, the weightingcoefficients cannot be adaptively optimized in response to thesignal-to-noise ratio of the resulting ABR measurement. In contrast, thedata-acquisition system 10 described herein enables weightingcoefficients 30 a-j to be assigned dynamically or after the fact,thereby providing considerably more flexibility in the selection ofmethods for optimizing signal-to-noise ratio of the ABR measurement 38.

The data-acquisition system 10 and method described herein are generallyapplicable to all clinical ABR testing, whether manual or automated.Such ABR testing can include neuro-diagnostic procedures, audiometricthreshold estimation, and newborn screening.

The invention has been described in the context of measuring auditoryresponse. However, evoked responses can arise from other stimuli, suchas visual, tactile, olfactory, or gustatory stimuli. The principlesdescribed herein are applicable to measurement of evoked responseresulting from whatever stimuli.

As described herein, samples are sorted into bins 22 a-j on the basis ofelectrophysiologic noise amplitudes. However, sorting parameters otherthan electrophysiologic noise amplitude can be used. Additionally, thesorting parameter can also be a multi-dimensional quantity. For example,the digital signal processor 20 may have a second input for measuringambient acoustic noise level. In such a case, the digital signalprocessor 20 can assign samples to bins 22 a-j on the basis of both anelectrophysiologic quantity, namely the sample amplitude, and on anacoustic quantity, namely the measured ambient acoustic noise level inthe testing room. In this case, the sorting parameter is a twodimensional quantity and the bins 22 a-j can be viewed as atwo-dimensional array. While this might complicate the implementation ofthe data-acquisition system 10, the principle of the invention is itselfunchanged.

Alternatively, the sorting parameter can be made a function of more thanone variable. For example, a measurement of ambient acoustic noise inthe room might be converted into an equivalent electrophysiologic noiselevel. This equivalent electrophysiologic noise level could then beadded to corresponding samples from the digital signal before thosesignals are sorted into bins 22 a-j.

It is to be understood that the foregoing description is intended toillustrate and not limit the scope of the invention. The invention isdefined by the scope of the following claims. Other aspects, advantages,and modifications are within the scope of the following claims.

Having described the invention, and a preferred embodiment thereof, whatI claim as new, and secured by Letters Patent is:
 1. A method ofestimating an electrophysiologic response contained in a measuredelectrophysiologic signal, said method comprising: obtaining a pluralityof samples of said measured electrophysiologic signal; defining aplurality of bins, each of said bins corresponding to a range of valuesof said sorting parameter; for each sample, classifying said sample intoone of said bins on the basis of a value of said sorting parameter, saidvalue being associated with said sample; for each bin, maintaining a binstatistic indicative of samples classified into said bin; and estimatingsaid electrophysiologic response by combining said bin statistics,wherein combining said bin statistics comprises selecting a subset ofsaid bin statistics to derive a quantity indicative of saidelectrophysiologic response.
 2. A method of estimating anelectrophysiologic response contained in a measured electrophysiologicsignal, said method comprising: obtaining a plurality of samples of saidmeasured electrophysiologic signal; defining a plurality of bins, eachof said bins corresponding to a range of values of a sorting parameter,said sorting parameter being selected to include a measure of noise insaid plurality of samples; for each sample, classifying said sample intoone of said bins on the basis of a value of said sorting parameter, saidvalue being associated with said sample; for each bin, maintaining a binstatistic indicative of samples classified into said bin; and estimatingsaid electrophysiologic response on the basis of said bin statistics. 3.The method of claim 1, wherein defining a plurality of bins comprisesselecting a range of values for each bin such that each value of saidsorting parameter is associated with at most one bin.
 4. The method ofclaim 1, wherein selecting said sorting parameter comprises selectingsaid sorting parameter to include a measure of electrophysiologic noisein said plurality of samples.
 5. The method of claim 1, whereinselecting said sorting parameter comprises selecting said sortingparameter to include a measure of ambient acoustic noise associated withsaid plurality of samples.
 6. The method of claim 1, wherein maintainingsaid bin statistic comprises maintaining a moving average of samples insaid bin.
 7. The method of claim 1, wherein estimating saidelectrophysiologic response comprises combining said bin statistics toderive a quantity indicative of said electrophysiologic response.
 8. Themethod of claim 7, wherein combining said bin statistics comprisesevaluating an average of said bin statistics.
 9. The method of claim 8,wherein evaluating an average of said bin statistics comprisesevaluating a weighted average of said bin statistics.
 10. The method ofclaim 7, wherein combining said bin statistics comprises selecting asubset of said bin statistics for deriving said quantity indicative ofsaid electrophysiologic response.
 11. The method of claim 7, whereincombining said bin statistics comprises selecting weights to apply toeach of said bin statistics.
 12. The method of claim 11, whereinselecting said weights comprises selecting said weights to optimize ameasure of an extent to which said quantity approximates saidelectrophysiologic response.
 13. The method of claim 11, whereinselecting said weights comprises selecting said weights on the basis ofa measure of a quality of samples in bins corresponding to each of saidweights.
 14. The method of claim 13, wherein selecting said weights onthe basis of a measure of quality comprises assigning a weight to aparticular bin on the basis of noise associated with samples in saidparticular bin.
 15. A method of estimating an electrophysiologicresponse contained in a measured electrophysiologic signal, said methodcomprising: obtaining a plurality of samples of said measuredelectrophysiologic signal; defining a plurality of bins, each of saidbins corresponding to a range of values of a sorting parameter; for eachsample, classifying said sample into one of said bins on the basis of avalue of said sorting parameter, said value being associated with saidsample; for each bin, maintaining a bin statistic indicative of samplesclassified into said bin; and estimating said electrophysiologicresponse by combining said bin statistics, wherein combining said binstatistics comprises selecting weights to apply to each of said binstatistics, said weights being selected to optimize a measure of anextent to which said quantity approximates said electrophysiologicresponse.
 16. A system for estimating an electrophysiologic responsecontained in a measured electrophysiologic signal, said systemcomprising: a digital signal processor configured to receive samples ofsaid measured electrophysiologic signal to define a plurality of bins,each of said bins corresponding to a range of a sorting parameter; toclassify each of said samples into one of said bins on the basis of avalue of said sorting parameter, said value being associated with saidsample, and to maintain a plurality of bin statistics, each of said binstatistics being indicative of samples classified into a correspondingbin, said digital signal processor including a noise analyzer forevaluating noise in said plurality of samples; a memory element incommunication with said digital signal processor, said memory elementbeing configured to store said bin statistics; and a processing elementin communication with said memory element, said processing element beingconfigured to estimate said electrophysiologic response on the basis ofsaid bin statistics.
 17. The system of claim 16, wherein said digitalsignal processor is configured to select a range of values for each binsuch that each value of said sorting parameter is associated with atmost one bin.
 18. The system of claim 16, wherein said noise analyzer isconfigured to evaluate a measure of electrophysiologic noise in saidplurality of samples.
 19. The system of claim 16, wherein said noiseanalyzer is configured to evaluate a measure of ambient acoustic noiseassociated with said plurality of samples.
 20. The system of claim 15,wherein said digital signal processor is configured to maintain a movingaverage of samples in said bin.
 21. The system of claim 15, wherein saidprocessing element is configured to estimate said electrophysiologicresponse by combining said bin statistics to derive a quantityindicative of said electrophysiologic response.
 22. The system of claim21, wherein said processing element is configured to evaluate an averageof said bin statistics.
 23. The system of claim 22, wherein saidprocessing element is configured to evaluate a weighted average of saidbin statistics.
 24. The system of claim 21, wherein said processingelement is configured to select a subset of said bin statistics forderiving said quantity indicative of said electrophysiologic response.25. The system of claim 21, wherein said processing element isconfigured to select weights to apply to each of said bin statistics.26. The system of claim 25, wherein said processing element isconfigured to select said weights on the basis of a measure of a qualityof samples in bins corresponding to each of said weights.
 27. The systemof claim 26, wherein said processing element is configured to assign aweight to a particular bin on the basis of noise associated with samplesin said particular bin.
 28. The system of claim 15, wherein said digitalsignal processor comprises a general purpose digital computer.
 29. Amethod of estimating an electrophysiologic response contained in ameasured electrophysiologic signal, said method comprising: obtaining aplurality of samples of said measured electrophysiologic signal;defining a plurality of bins, each of said bins corresponding to a rangeof values of a sorting parameter; for each sample, classifying saidsample into one of said bins on the basis of a value of said sortingparameter, said value being associated with said sample; for each bin,maintaining a bin statistic indicative of samples classified into saidbin; and estimating said electrophysiologic response by combining saidbin statistics, wherein combining said bin statistics includes selectingweights to apply to each of said bin statistics, said weights beingselected on the basis of a measure of a quality of samples in binscorresponding to each of said weights.
 30. A system for estimating anelectrophysiologic response contained in a measured electrophysiologicsignal, said system comprising: a digital signal processor configured toreceive samples of said measured electrophysiologic signal to define aplurality of bins, each of said bins corresponding to a range of asorting parameter; to classify each of said samples into one of saidbins on the basis of a value of said sorting parameter, said value beingassociated with said sample, and to maintain a plurality of binstatistics, each of said bin statistics being indicative of samplesclassified into a corresponding bin; a memory element in communicationwith said digital signal processor, said memory element being configuredto store said bin statistics; and a processing element in communicationwith said memory element, said processing element being configured toestimate said electrophysiologic response by combining bin statistics toderive a quantity indicative of said electrophysiological response, saidquantity being derived by selecting a subset of said bin statistics forderiving a quantity indicative of said electrophysiologic response. 31.A system for estimating an electrophysiologic response contained in ameasured electrophysiologic signal, said system comprising: a digitalsignal processor configured to receive samples of said measuredelectrophysiologic signal to define a plurality of bins, each of saidbins corresponding to a range of a sorting parameter; to classify eachof said samples into one of said bins on the basis of a value of saidsorting parameter, said value being associated with said sample, and tomaintain a plurality of bin statistics, each of said bin statisticsbeing indicative of samples classified into a corresponding bin; amemory element in communication with said digital signal processor, saidmemory element being configured to store said bin statistics; and aprocessing element in communication with said memory element, saidprocessing element being configured to estimate said electrophysiologicresponse by combining bin statistics to derive a quantity indicative ofsaid electrophysiological response, said quantity being derived byselecting weights to apply to each of said bin statistics, said weightsbeing selected on the basis of a measure of a quality of samples in binscorresponding to each of said weights.